They should validate each class of input separately and never let free-form text shape privileged operations. URLs need allowlists, file paths need traversal checks, and generated SQL needs post-generation inspection before execution. The safest approach is to keep data, templates, and commands in different trust zones.
Why URL, Path, and Query Handling Needs Separate Controls
AI applications blur the line between user input and system action, so URL, path, and query handling must be treated as distinct trust decisions. A URL can trigger outbound access, a path can redirect file reads or writes, and a generated query can become an execution path. The core failure is not “bad text”, it is letting one untrusted string influence a privileged operation without the right validation boundary. That is why the safest design keeps data, templates, and commands in different trust zones.
For AI systems this matters because model output is often persuasive, but persuasion is not permission. A generated URL may look legitimate while still pointing to an attacker-controlled endpoint, a generated path may escape the intended directory, and a generated query may still need inspection before execution. Teams that treat all three as one generic sanitisation problem usually miss the different exploit conditions each one creates.
One useful reference point is the OWASP Non-Human Identity Top 10, which reinforces the need to bound machine-driven actions rather than trusting the originating text. In practice, many security teams only discover the difference between input and authority after an agent has already reached a privileged endpoint or filesystem target.
How It Works in Practice
Strong handling starts by separating validation rules by input class. URLs should be compared against a known allowlist of hosts, schemes, and destination patterns, with redirects and internal network targets controlled explicitly. Paths should be normalised and checked against traversal patterns before any file access, and the final resolved path should still be verified against the intended directory boundary. Queries need a stricter treatment: even when the model generates valid syntax, the application should inspect the output, bind parameters where possible, and only execute statements that conform to the intended operation.
- Validate URLs against approved domains, schemes, and ports before any fetch or webhook call.
- Canonicalise file paths, then reject relative traversal, alternate separators, and unexpected resolution targets.
- Inspect generated SQL or command-like output after generation, before execution, and fail closed on ambiguity.
- Keep retrieval content, prompt text, and operational commands in separate processing paths.
AI-specific risk often appears when one component is asked to do too much, such as translating user intent directly into a tool call. That pattern makes it easy for prompt injection or malformed input to steer a downstream action even when the model itself appears “confident”. Teams should design the tool layer so that the model can propose, but a deterministic control can still approve or reject the action.
For teams managing secrets-heavy application workflows, the The State of Secrets in AppSec report is useful because it highlights how often application security breaks down when sensitive operations are not tightly governed. These controls tend to break down when developers let a single parser or filter stand in for separate URL, path, and execution checks, because each class fails in a different way.
Common Variations and Edge Cases
Tighter validation often increases implementation overhead, requiring teams to balance flexibility against blast-radius control. The main variation is whether the application only reads data, or whether it can also create side effects, reach internal services, or touch local files. Once any of those are possible, a permissive “let the model decide” design becomes much harder to defend.
There is also a genuine trade-off between user convenience and safety. Auto-converting free-form text into a usable URL, path, or query can improve workflows, but it also increases the chance that an attacker or a confused user pushes the system into an unintended trust boundary. Best practice is evolving toward explicit confirmation for higher-risk actions, especially where the generated output affects network access, filesystem access, or data modification.
Another edge case is that a technically valid string may still be operationally unsafe. For example, a URL can be syntactically correct but point to an internal service, a path can resolve correctly but escape the intended sandbox after canonicalisation, and a query can be well-formed but still expose more data than the user should see. The practical rule is to validate syntax first, then enforce destination, scope, and privilege separately. This approach matters most in systems that combine retrieval, tool use, and automated execution because the failure mode is usually privilege misuse, not parsing failure.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and MITRE ATT&CK address the attack and risk surface, while CIS Controls v8 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A2 — Tool and Action Authorization | Covers preventing model output from directly triggering privileged tools or actions. |
| A3 — Input and Output Validation | Applies to validating model inputs and outputs before they affect URLs, paths, or queries. | |
| Recommendation — Gate tool use behind explicit authorization and approval checks before executing model-generated actions. Validate generated output by type and reject any URL, path, or query that violates policy. | ||
| CIS Controls v8 | 3 — Data Protection | Relevant because unsafe paths and queries can expose or modify protected data. |
| Recommendation — Protect sensitive data by separating templates, data, and execution paths. | ||
| MITRE ATT&CK | T1190 — Exploit Public-Facing Application | Covers abuse of exposed AI application inputs to reach unintended internal actions. |
| Recommendation — Harden exposed AI inputs to prevent attackers from steering application-side execution. | ||
Practitioner Guidance
What to prioritise: Treat outbound URL handling, filesystem access, and query execution as three different control problems. If one review passes all three through the same safety check, that is a design smell worth fixing first.
What to verify: Confirm that the application can prove which hosts, directories, and query templates are allowed before any runtime action occurs. If the model can influence the destination but the system cannot independently constrain it, the control is too weak.
Decision rule: If the generated output can change where data goes, what file is read, or what data is returned, require deterministic post-generation enforcement rather than relying on prompt instructions alone.
Practitioner takeaway: The security goal is not to make AI output “safe text”, it is to ensure untrusted text never becomes a privileged instruction without a separate, enforceable check.
Related resources from NHI Mgmt Group
- What steps should security teams take to prevent Shadow AI risks?
- How should security teams handle AI agents that need to log into SaaS applications?
- How should security teams test AI-enabled applications when web, API, and model-driven paths are interconnected?
- How should security teams handle broken access control when AI agents can cross from web permissions into tool execution paths?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on September 14, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
